Training Minimum Enclosing Balls for Cross Tasks Knowledge Transfer

  • Shaoning Pang
  • Fan Liu
  • Youki Kadobayashi
  • Tao Ban
  • Daisuke Inoue
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7663)


This paper proposes a learner independent multi-task learning (MTL) scheme such that \(\mathcal{M}_\mathcal{L} = \mathcal{L}(T^i, KT(T^i, T^j))\), for i,j = 1,2, i ≠ j, where KT is independent to the learner \(\mathcal{L}\), and MTL is conducted for arbitrary learner combinations. In the proposed solution, we use Minimum Enclosing Balls (MEBs) as knowledge carriers to extract and transfer knowledge from one task to another. Since the knowledge presented in MEB can be decomposed as raw data, it can be incorporated into any learner as additional training data for a new learning task and thus improve its learning rate. The effectiveness and robustness of the proposed KT is evaluated on multi-task pattern recognition (MTPR) problems derived from UCI datasets, using classifiers from different disciplines for MTL. The experimental results show that multi-task learners using KT via MEB carriers perform better than learners without-KT, and it is successfully applied to all type of classifiers.


Minimum enclosing ball Multi-task learning Cross tasks knowledge transfer 


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  1. 1.
    Kumar, P., Mitchell, J.S.B., Yildirim, E.A.: Approximate Minimum Enclosing Balls in High Dimensions Using Core-sets. Journal of Experimental Algorithmics, 8(1.1) (2003)Google Scholar
  2. 2.
    Welzl, E.: Smallest Enclosing Disks (Balls and Ellipsoids). Results and New Trends in Computer Science pp. 359–370 (1991)Google Scholar
  3. 3.
    Chan, T.M.: Approximating the Diameter, Width, Smallest Enclosing Cylinder, and Minimum-width Annulus. In: Proceedings of the 16th Annual Symposium on Computational Geometry, Clear Water Bay, Kowloon, Hong Kong, pp. 300–309. ACM, New York (2000)Google Scholar
  4. 4.
    Megiddo, N.: Linear-time Algorithms for Linear Programming in r 3 and Related Problems. SIAM Journal on Computing 12(4), 759–776 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Badoiu, M.: Optimal Core Sets for Balls. In: DIMACS Workshop on Computational Geometry (2002)Google Scholar
  6. 6.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised Learning. MIT Press, Cambridge (2006)Google Scholar
  7. 7.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shaoning Pang
    • 1
  • Fan Liu
    • 2
  • Youki Kadobayashi
    • 3
  • Tao Ban
    • 4
  • Daisuke Inoue
    • 4
  1. 1.Department of ComputingUnitec Institute of TechnologyAucklandNew Zealand
  2. 2.School of Computing and Mathematical SciencesAuckland University of TechnologyNew Zealand
  3. 3.Graduate School of Information ScienceNara Institute of Science and TechnologyJapan
  4. 4.Cybersecurity LaboratoryNational Institution of Information and Communications TechnologyJapan

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